Overview

Dataset statistics

Number of variables21
Number of observations13642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory168.0 B

Variable types

Categorical2
Numeric19

Alerts

Date has a high cardinality: 718 distinct values High cardinality
0.1 is highly correlated with 0.2 and 17 other fieldsHigh correlation
0.2 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.9 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.1 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.2 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.9 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.1 is highly correlated with 0.2 and 14 other fieldsHigh correlation
0.2 is highly correlated with 0.1 and 16 other fieldsHigh correlation
0.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.9 is highly correlated with 0.2 and 16 other fieldsHigh correlation
1 is highly correlated with 0.3 and 14 other fieldsHigh correlation
1.1 is highly correlated with 0.2 and 16 other fieldsHigh correlation
1.2 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.9 is highly correlated with 0.1 and 16 other fieldsHigh correlation
0.1 is highly correlated with 0.2 and 14 other fieldsHigh correlation
0.2 is highly correlated with 0.1 and 15 other fieldsHigh correlation
0.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
0.9 is highly correlated with 0.2 and 16 other fieldsHigh correlation
1 is highly correlated with 0.3 and 14 other fieldsHigh correlation
1.1 is highly correlated with 0.3 and 15 other fieldsHigh correlation
1.2 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.3 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.4 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.5 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.6 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.7 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.8 is highly correlated with 0.1 and 17 other fieldsHigh correlation
1.9 is highly correlated with 0.1 and 16 other fieldsHigh correlation
Tenor is highly correlated with 0.1 and 18 other fieldsHigh correlation
0.1 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.2 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.3 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.4 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.5 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.6 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.7 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.8 is highly correlated with Tenor and 18 other fieldsHigh correlation
0.9 is highly correlated with Tenor and 18 other fieldsHigh correlation
1 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.1 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.2 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.3 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.4 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.5 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.6 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.7 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.8 is highly correlated with Tenor and 18 other fieldsHigh correlation
1.9 is highly correlated with Tenor and 18 other fieldsHigh correlation
Date is uniformly distributed Uniform
Tenor is uniformly distributed Uniform
0.1 has unique values Unique
0.2 has unique values Unique
0.3 has unique values Unique
0.5 has unique values Unique
0.6 has unique values Unique
0.7 has unique values Unique
1.1 has unique values Unique
1.9 has unique values Unique

Reproduction

Analysis started2022-01-04 09:26:57.424073
Analysis finished2022-01-04 09:28:11.716540
Duration1 minute and 14.29 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct718
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
1/5/2017
 
19
11/27/2018
 
19
10/30/2018
 
19
10/31/2018
 
19
11/1/2018
 
19
Other values (713)
13547 

Length

Max length10
Median length9
Mean length8.903899721
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/5/2017
2nd row1/5/2017
3rd row1/5/2017
4th row1/5/2017
5th row1/5/2017

Common Values

ValueCountFrequency (%)
1/5/201719
 
0.1%
11/27/201819
 
0.1%
10/30/201819
 
0.1%
10/31/201819
 
0.1%
11/1/201819
 
0.1%
11/2/201819
 
0.1%
11/5/201819
 
0.1%
11/6/201819
 
0.1%
11/7/201819
 
0.1%
11/8/201819
 
0.1%
Other values (708)13452
98.6%

Length

2022-01-04T14:58:11.847325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1/5/201719
 
0.1%
3/7/201719
 
0.1%
2/3/201719
 
0.1%
1/19/201719
 
0.1%
1/9/201719
 
0.1%
1/10/201719
 
0.1%
1/11/201719
 
0.1%
1/12/201719
 
0.1%
1/13/201719
 
0.1%
1/16/201719
 
0.1%
Other values (708)13452
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tenor
Categorical

HIGH CORRELATION
UNIFORM

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
2M
 
718
7Y
 
718
30Y
 
718
25Y
 
718
20Y
 
718
Other values (14)
10052 

Length

Max length3
Median length2
Mean length2.315789474
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2M
2nd row3M
3rd row6M
4th row9M
5th row1Y

Common Values

ValueCountFrequency (%)
2M718
 
5.3%
7Y718
 
5.3%
30Y718
 
5.3%
25Y718
 
5.3%
20Y718
 
5.3%
15Y718
 
5.3%
10Y718
 
5.3%
9Y718
 
5.3%
8Y718
 
5.3%
6Y718
 
5.3%
Other values (9)6462
47.4%

Length

2022-01-04T14:58:12.002509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2m718
 
5.3%
3m718
 
5.3%
6m718
 
5.3%
9m718
 
5.3%
1y718
 
5.3%
2y718
 
5.3%
3y718
 
5.3%
4y718
 
5.3%
5y718
 
5.3%
6y718
 
5.3%
Other values (9)6462
47.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

0.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3318807811
Minimum0.205536244
Maximum0.687276568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:12.176237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.205536244
5-th percentile0.2301580772
Q10.2845775915
median0.316036176
Q30.3519498282
95-th percentile0.5244997588
Maximum0.687276568
Range0.481740324
Interquartile range (IQR)0.06737223675

Descriptive statistics

Standard deviation0.08341089798
Coefficient of variation (CV)0.2513278947
Kurtosis1.867888186
Mean0.3318807811
Median Absolute Deviation (MAD)0.034297645
Skewness1.36814667
Sum4527.517615
Variance0.006957377902
MonotonicityNot monotonic
2022-01-04T14:58:12.383149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4682136791
 
< 0.1%
0.3666866591
 
< 0.1%
0.3206259631
 
< 0.1%
0.3174280261
 
< 0.1%
0.3141326461
 
< 0.1%
0.308095091
 
< 0.1%
0.3026661111
 
< 0.1%
0.297366191
 
< 0.1%
0.2930932311
 
< 0.1%
0.2894924861
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.2055362441
< 0.1%
0.2066093641
< 0.1%
0.206614421
< 0.1%
0.2066188181
< 0.1%
0.2066234851
< 0.1%
0.2073234991
< 0.1%
0.2073294941
< 0.1%
0.2073342231
< 0.1%
0.2073391211
< 0.1%
0.2075769361
< 0.1%
ValueCountFrequency (%)
0.6872765681
< 0.1%
0.6810105571
< 0.1%
0.6796275781
< 0.1%
0.6769504481
< 0.1%
0.6758908751
< 0.1%
0.6737495741
< 0.1%
0.6726694161
< 0.1%
0.6692524381
< 0.1%
0.6689736881
< 0.1%
0.6675054471
< 0.1%

0.2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3020067868
Minimum0.189888887
Maximum0.630411466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:12.592989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.189888887
5-th percentile0.2095546128
Q10.2606050265
median0.288830994
Q30.3239870847
95-th percentile0.4649559894
Maximum0.630411466
Range0.440522579
Interquartile range (IQR)0.06338205825

Descriptive statistics

Standard deviation0.0720032117
Coefficient of variation (CV)0.2384158729
Kurtosis1.836474857
Mean0.3020067868
Median Absolute Deviation (MAD)0.032861562
Skewness1.270970936
Sum4119.976586
Variance0.005184462495
MonotonicityNot monotonic
2022-01-04T14:58:12.788192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4192506961
 
< 0.1%
0.3230284441
 
< 0.1%
0.2876084951
 
< 0.1%
0.285165581
 
< 0.1%
0.2824518861
 
< 0.1%
0.2770946831
 
< 0.1%
0.2723522141
 
< 0.1%
0.2673379491
 
< 0.1%
0.2632985131
 
< 0.1%
0.2599128081
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1898888871
< 0.1%
0.1898922411
< 0.1%
0.1898951891
< 0.1%
0.1899010781
< 0.1%
0.1910652681
< 0.1%
0.1910993991
< 0.1%
0.191141621
< 0.1%
0.1916389491
< 0.1%
0.1916435011
< 0.1%
0.1916456061
< 0.1%
ValueCountFrequency (%)
0.6304114661
< 0.1%
0.6248797571
< 0.1%
0.6224666981
< 0.1%
0.617850351
< 0.1%
0.6158222691
< 0.1%
0.6146630611
< 0.1%
0.6132583411
< 0.1%
0.612642661
< 0.1%
0.6101162261
< 0.1%
0.6096858931
< 0.1%

0.3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2766141094
Minimum0.172410577
Maximum0.578715254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:12.987294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.172410577
5-th percentile0.1932472587
Q10.2396629238
median0.265497186
Q30.300551673
95-th percentile0.4122838509
Maximum0.578715254
Range0.406304677
Interquartile range (IQR)0.06088874925

Descriptive statistics

Standard deviation0.06242792204
Coefficient of variation (CV)0.2256859644
Kurtosis1.819528749
Mean0.2766141094
Median Absolute Deviation (MAD)0.0318053445
Skewness1.152233516
Sum3773.56968
Variance0.003897245451
MonotonicityNot monotonic
2022-01-04T14:58:13.187374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3762598881
 
< 0.1%
0.2870897841
 
< 0.1%
0.2608047151
 
< 0.1%
0.2590277351
 
< 0.1%
0.2568294121
 
< 0.1%
0.2520540921
 
< 0.1%
0.2478998321
 
< 0.1%
0.2431332821
 
< 0.1%
0.2393026221
 
< 0.1%
0.2361147671
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1724105771
< 0.1%
0.1724166571
< 0.1%
0.1724177971
< 0.1%
0.1724246131
< 0.1%
0.1724684921
< 0.1%
0.1727227771
< 0.1%
0.1730158761
< 0.1%
0.1730179181
< 0.1%
0.1730228511
< 0.1%
0.1730293121
< 0.1%
ValueCountFrequency (%)
0.5787152541
< 0.1%
0.5737834441
< 0.1%
0.5727378231
< 0.1%
0.5678737421
< 0.1%
0.5632754321
< 0.1%
0.5625458721
< 0.1%
0.5611711391
< 0.1%
0.5581243581
< 0.1%
0.5580354081
< 0.1%
0.5580284651
< 0.1%

0.4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2545995648
Minimum0.156771771
Maximum0.53134445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:13.400281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.156771771
5-th percentile0.1775628123
Q10.2218273853
median0.2456310865
Q30.2806095375
95-th percentile0.3657596063
Maximum0.53134445
Range0.374572679
Interquartile range (IQR)0.05878215225

Descriptive statistics

Standard deviation0.05432004986
Coefficient of variation (CV)0.2133548418
Kurtosis1.813606725
Mean0.2545995648
Median Absolute Deviation (MAD)0.030362648
Skewness1.015516021
Sum3473.247263
Variance0.002950667817
MonotonicityNot monotonic
2022-01-04T14:58:13.596457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2720948662
 
< 0.1%
0.3381583011
 
< 0.1%
0.1715640561
 
< 0.1%
0.2384661431
 
< 0.1%
0.2372766341
 
< 0.1%
0.2355334121
 
< 0.1%
0.2312591121
 
< 0.1%
0.2276146681
 
< 0.1%
0.2230596131
 
< 0.1%
0.2194116121
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1567717711
< 0.1%
0.1567778421
< 0.1%
0.1567791951
< 0.1%
0.1567860691
< 0.1%
0.1572250941
< 0.1%
0.1576265691
< 0.1%
0.1576752621
< 0.1%
0.1576796581
< 0.1%
0.1576821411
< 0.1%
0.1576853951
< 0.1%
ValueCountFrequency (%)
0.531344451
< 0.1%
0.52702811
< 0.1%
0.5269084261
< 0.1%
0.5219811911
< 0.1%
0.516024331
< 0.1%
0.5151824651
< 0.1%
0.5139794651
< 0.1%
0.512808611
< 0.1%
0.5107024691
< 0.1%
0.5103557841
< 0.1%

0.5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2352753803
Minimum0.143880871
Maximum0.487723209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:13.817034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.143880871
5-th percentile0.1642507532
Q10.2065266615
median0.2281314965
Q30.262412015
95-th percentile0.3230431956
Maximum0.487723209
Range0.343842338
Interquartile range (IQR)0.0558853535

Descriptive statistics

Standard deviation0.04752165724
Coefficient of variation (CV)0.2019831279
Kurtosis1.813153518
Mean0.2352753803
Median Absolute Deviation (MAD)0.028794879
Skewness0.8737569113
Sum3209.626738
Variance0.002258307907
MonotonicityNot monotonic
2022-01-04T14:58:14.013210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3043721371
 
< 0.1%
0.2304762661
 
< 0.1%
0.2195667941
 
< 0.1%
0.2188888871
 
< 0.1%
0.2175423011
 
< 0.1%
0.2136982191
 
< 0.1%
0.2104965351
 
< 0.1%
0.206119731
 
< 0.1%
0.2026293111
 
< 0.1%
0.1997793671
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1438808711
< 0.1%
0.1438855261
< 0.1%
0.1438882241
< 0.1%
0.1438937181
< 0.1%
0.1445983631
< 0.1%
0.1447844871
< 0.1%
0.1449239031
< 0.1%
0.1449270211
< 0.1%
0.1449307291
< 0.1%
0.1449326641
< 0.1%
ValueCountFrequency (%)
0.4877232091
< 0.1%
0.4848364461
< 0.1%
0.4836989581
< 0.1%
0.479658341
< 0.1%
0.4731405021
< 0.1%
0.4709330051
< 0.1%
0.470907351
< 0.1%
0.4704792591
< 0.1%
0.4675128911
< 0.1%
0.4671072441
< 0.1%

0.6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2182174388
Minimum0.133259019
Maximum0.44750975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:14.221097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.133259019
5-th percentile0.1527150495
Q10.193466998
median0.2117913055
Q30.2455552923
95-th percentile0.287529975
Maximum0.44750975
Range0.314250731
Interquartile range (IQR)0.05208829425

Descriptive statistics

Standard deviation0.04201143871
Coefficient of variation (CV)0.1925209962
Kurtosis1.791315475
Mean0.2182174388
Median Absolute Deviation (MAD)0.026931003
Skewness0.7544421399
Sum2976.9223
Variance0.001764960982
MonotonicityNot monotonic
2022-01-04T14:58:14.416300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2748226611
 
< 0.1%
0.2078192511
 
< 0.1%
0.203489981
 
< 0.1%
0.2032421011
 
< 0.1%
0.2022305281
 
< 0.1%
0.1987500741
 
< 0.1%
0.195929071
 
< 0.1%
0.1917003281
 
< 0.1%
0.1883440391
 
< 0.1%
0.1856359571
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1332590191
< 0.1%
0.1332608281
< 0.1%
0.1332660481
< 0.1%
0.1332687051
< 0.1%
0.1341338021
< 0.1%
0.1341433581
< 0.1%
0.1343731591
< 0.1%
0.1343737741
< 0.1%
0.1343791651
< 0.1%
0.1343797161
< 0.1%
ValueCountFrequency (%)
0.447509751
< 0.1%
0.4458724411
< 0.1%
0.4438262911
< 0.1%
0.440609941
< 0.1%
0.4335707891
< 0.1%
0.4322071291
< 0.1%
0.430318831
< 0.1%
0.4300644761
< 0.1%
0.4298326731
< 0.1%
0.4269483861
< 0.1%

0.7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.203196054
Minimum0.124633792
Maximum0.410605165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:14.769611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.124633792
5-th percentile0.142803857
Q10.181452824
median0.1974021035
Q30.2293350278
95-th percentile0.2621491356
Maximum0.410605165
Range0.285971373
Interquartile range (IQR)0.04788220375

Descriptive statistics

Standard deviation0.03785725327
Coefficient of variation (CV)0.1863089982
Kurtosis1.669523616
Mean0.203196054
Median Absolute Deviation (MAD)0.0240605285
Skewness0.6918374653
Sum2772.000569
Variance0.001433171625
MonotonicityNot monotonic
2022-01-04T14:58:14.957014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2500659381
 
< 0.1%
0.1884134391
 
< 0.1%
0.1898734341
 
< 0.1%
0.1899598041
 
< 0.1%
0.1892133631
 
< 0.1%
0.1860295481
 
< 0.1%
0.1835270311
 
< 0.1%
0.1794193521
 
< 0.1%
0.1761755871
 
< 0.1%
0.1735930621
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1246337921
< 0.1%
0.124636281
< 0.1%
0.1246410741
< 0.1%
0.1246427341
< 0.1%
0.1254853941
< 0.1%
0.1256053421
< 0.1%
0.1257579881
< 0.1%
0.1257611141
< 0.1%
0.1257629711
< 0.1%
0.125767191
< 0.1%
ValueCountFrequency (%)
0.4106051651
< 0.1%
0.4100684751
< 0.1%
0.4071993671
< 0.1%
0.4047736311
< 0.1%
0.3972351791
< 0.1%
0.3965654211
< 0.1%
0.3952666041
< 0.1%
0.3933961551
< 0.1%
0.3925289891
< 0.1%
0.390316521
< 0.1%

0.8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13640
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1901629589
Minimum0.117846214
Maximum0.377628354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:15.154158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.117846214
5-th percentile0.1347087577
Q10.1698074727
median0.1836891815
Q30.2150780663
95-th percentile0.2444608031
Maximum0.377628354
Range0.25978214
Interquartile range (IQR)0.0452705935

Descriptive statistics

Standard deviation0.03512530245
Coefficient of variation (CV)0.1847115898
Kurtosis1.337634467
Mean0.1901629589
Median Absolute Deviation (MAD)0.0214551495
Skewness0.6897491121
Sum2594.203085
Variance0.001233786872
MonotonicityNot monotonic
2022-01-04T14:58:15.352284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1798687932
 
< 0.1%
0.1610788242
 
< 0.1%
0.2315353431
 
< 0.1%
0.1480096251
 
< 0.1%
0.1723738531
 
< 0.1%
0.1771295681
 
< 0.1%
0.1785195671
 
< 0.1%
0.1788228071
 
< 0.1%
0.1782590851
 
< 0.1%
0.1753012131
 
< 0.1%
Other values (13630)13630
99.9%
ValueCountFrequency (%)
0.1178462141
< 0.1%
0.117852631
< 0.1%
0.117854311
< 0.1%
0.1178599471
< 0.1%
0.1186771681
< 0.1%
0.1188329551
< 0.1%
0.1189248011
< 0.1%
0.1189292271
< 0.1%
0.1189327931
< 0.1%
0.1189381821
< 0.1%
ValueCountFrequency (%)
0.3776283541
< 0.1%
0.3772049831
< 0.1%
0.3740178721
< 0.1%
0.3723731351
< 0.1%
0.3643569891
< 0.1%
0.3642238031
< 0.1%
0.3639881041
< 0.1%
0.3599226721
< 0.1%
0.3585025591
< 0.1%
0.3570672021
< 0.1%

0.9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1792925081
Minimum0.112773344
Maximum0.349102109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:15.554318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.112773344
5-th percentile0.1281335977
Q10.1573503465
median0.172806795
Q30.2037042755
95-th percentile0.2341193551
Maximum0.349102109
Range0.236328765
Interquartile range (IQR)0.046353929

Descriptive statistics

Standard deviation0.03367421826
Coefficient of variation (CV)0.1878172079
Kurtosis0.8241667633
Mean0.1792925081
Median Absolute Deviation (MAD)0.0211406935
Skewness0.6880317734
Sum2445.908395
Variance0.001133952976
MonotonicityNot monotonic
2022-01-04T14:58:15.755372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1732652412
 
< 0.1%
0.2214035571
 
< 0.1%
0.1238755721
 
< 0.1%
0.1674240061
 
< 0.1%
0.1693276841
 
< 0.1%
0.1697060641
 
< 0.1%
0.169228641
 
< 0.1%
0.16642111
 
< 0.1%
0.1643506161
 
< 0.1%
0.1604199171
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1127733441
< 0.1%
0.1127786731
< 0.1%
0.1127879451
< 0.1%
0.1127925681
< 0.1%
0.1135811871
< 0.1%
0.1137081811
< 0.1%
0.113754681
< 0.1%
0.1137584291
< 0.1%
0.1137683211
< 0.1%
0.1137728551
< 0.1%
ValueCountFrequency (%)
0.3491021091
< 0.1%
0.3478839351
< 0.1%
0.3448592061
< 0.1%
0.3439994911
< 0.1%
0.3365000911
< 0.1%
0.3357706841
< 0.1%
0.3355604441
< 0.1%
0.3305343591
< 0.1%
0.3300967961
< 0.1%
0.3286331381
< 0.1%

1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13640
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1711099154
Minimum0.109261849
Maximum0.32542324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:15.961307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.109261849
5-th percentile0.1229677595
Q10.1482841072
median0.165295607
Q30.1947479805
95-th percentile0.2260917783
Maximum0.32542324
Range0.216161391
Interquartile range (IQR)0.04646387325

Descriptive statistics

Standard deviation0.03275067855
Coefficient of variation (CV)0.1914014069
Kurtosis0.413978914
Mean0.1711099154
Median Absolute Deviation (MAD)0.0217219845
Skewness0.6513278704
Sum2334.281466
Variance0.001072606945
MonotonicityNot monotonic
2022-01-04T14:58:16.152604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2079330832
 
< 0.1%
0.1308850322
 
< 0.1%
0.2209334051
 
< 0.1%
0.1186741121
 
< 0.1%
0.1622273811
 
< 0.1%
0.1625218151
 
< 0.1%
0.1620242581
 
< 0.1%
0.1592885251
 
< 0.1%
0.1573199951
 
< 0.1%
0.1534554041
 
< 0.1%
Other values (13630)13630
99.9%
ValueCountFrequency (%)
0.1092618491
< 0.1%
0.1092659691
< 0.1%
0.1092831791
< 0.1%
0.1092866871
< 0.1%
0.1100425881
< 0.1%
0.1100902171
< 0.1%
0.1101057751
< 0.1%
0.1101087841
< 0.1%
0.1101252971
< 0.1%
0.1101288821
< 0.1%
ValueCountFrequency (%)
0.325423241
< 0.1%
0.3236444071
< 0.1%
0.3207311241
< 0.1%
0.3206476681
< 0.1%
0.3136664061
< 0.1%
0.3122253761
< 0.1%
0.3119293841
< 0.1%
0.3108225111
< 0.1%
0.3079277281
< 0.1%
0.3078303121
< 0.1%

1.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1661202776
Minimum0.107101371
Maximum0.307730505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:16.357567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.107101371
5-th percentile0.1193238317
Q10.1443880515
median0.1602329325
Q30.18845217
95-th percentile0.2206061489
Maximum0.307730505
Range0.200629134
Interquartile range (IQR)0.0440641185

Descriptive statistics

Standard deviation0.03142234108
Coefficient of variation (CV)0.189154157
Kurtosis0.2801329903
Mean0.1661202776
Median Absolute Deviation (MAD)0.0203713775
Skewness0.6360555405
Sum2266.212828
Variance0.0009873635192
MonotonicityNot monotonic
2022-01-04T14:58:16.562526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2284180421
 
< 0.1%
0.1493006681
 
< 0.1%
0.1571128111
 
< 0.1%
0.1571660161
 
< 0.1%
0.1565426541
 
< 0.1%
0.1538028891
 
< 0.1%
0.151857691
 
< 0.1%
0.1480517931
 
< 0.1%
0.1450937351
 
< 0.1%
0.1428740161
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1071013711
< 0.1%
0.1071042871
< 0.1%
0.1071289581
< 0.1%
0.1071313641
< 0.1%
0.1077849291
< 0.1%
0.1077872051
< 0.1%
0.1077876091
< 0.1%
0.107809971
< 0.1%
0.1078126031
< 0.1%
0.1078516091
< 0.1%
ValueCountFrequency (%)
0.3077305051
< 0.1%
0.305722071
< 0.1%
0.3035070451
< 0.1%
0.3028823211
< 0.1%
0.2965438391
< 0.1%
0.2958512471
< 0.1%
0.2948531691
< 0.1%
0.2947878131
< 0.1%
0.2925579841
< 0.1%
0.2924557051
< 0.1%

1.2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1637185493
Minimum0.106038705
Maximum0.296793631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:16.785055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.106038705
5-th percentile0.116936661
Q10.1443298572
median0.1578262125
Q30.1850573433
95-th percentile0.2177693686
Maximum0.296793631
Range0.190754926
Interquartile range (IQR)0.040727486

Descriptive statistics

Standard deviation0.03040122289
Coefficient of variation (CV)0.185691988
Kurtosis0.2621540873
Mean0.1637185493
Median Absolute Deviation (MAD)0.019228105
Skewness0.5828122652
Sum2233.44845
Variance0.0009242343535
MonotonicityNot monotonic
2022-01-04T14:58:17.001725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1891224962
 
< 0.1%
0.15315491
 
< 0.1%
0.15347991
 
< 0.1%
0.1526404021
 
< 0.1%
0.1498310341
 
< 0.1%
0.1478372011
 
< 0.1%
0.1440870591
 
< 0.1%
0.1411751991
 
< 0.1%
0.1390157081
 
< 0.1%
0.1260280721
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1060387051
< 0.1%
0.1060405241
< 0.1%
0.1060716041
< 0.1%
0.1060730141
< 0.1%
0.1065567241
< 0.1%
0.1065583351
< 0.1%
0.1065675371
< 0.1%
0.10658651
< 0.1%
0.1065882621
< 0.1%
0.1067400351
< 0.1%
ValueCountFrequency (%)
0.2967936311
< 0.1%
0.2949313961
< 0.1%
0.2933193691
< 0.1%
0.2921458691
< 0.1%
0.2858494981
< 0.1%
0.2855347591
< 0.1%
0.2849564721
< 0.1%
0.2845024521
< 0.1%
0.281902781
< 0.1%
0.2818387251
< 0.1%

1.3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13640
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1629655491
Minimum0.105816316
Maximum0.292353183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:17.231093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.105816316
5-th percentile0.1155485485
Q10.1430215698
median0.1581576595
Q30.1838252157
95-th percentile0.2169722434
Maximum0.292353183
Range0.186536867
Interquartile range (IQR)0.040803646

Descriptive statistics

Standard deviation0.03057482932
Coefficient of variation (CV)0.1876152935
Kurtosis0.1852767953
Mean0.1629655491
Median Absolute Deviation (MAD)0.0203922835
Skewness0.5142253911
Sum2223.176021
Variance0.0009348201881
MonotonicityNot monotonic
2022-01-04T14:58:17.434093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1312037022
 
< 0.1%
0.14677172
 
< 0.1%
0.1107049841
 
< 0.1%
0.1520166951
 
< 0.1%
0.1512421271
 
< 0.1%
0.1501230991
 
< 0.1%
0.1471949591
 
< 0.1%
0.145094451
 
< 0.1%
0.1413993511
 
< 0.1%
0.1385284871
 
< 0.1%
Other values (13630)13630
99.9%
ValueCountFrequency (%)
0.1058163161
< 0.1%
0.1058172021
< 0.1%
0.1058534231
< 0.1%
0.1058539911
< 0.1%
0.1061769191
< 0.1%
0.1061779651
< 0.1%
0.1061887081
< 0.1%
0.1062104851
< 0.1%
0.10621151
< 0.1%
0.1062699061
< 0.1%
ValueCountFrequency (%)
0.2923531831
< 0.1%
0.2909263761
< 0.1%
0.2896987871
< 0.1%
0.2881875741
< 0.1%
0.2819626271
< 0.1%
0.2813455141
< 0.1%
0.2808417581
< 0.1%
0.2805940661
< 0.1%
0.2797585791
< 0.1%
0.2771189021
< 0.1%

1.4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1632808952
Minimum0.105808931
Maximum0.293139123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:17.815709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.105808931
5-th percentile0.1149738519
Q10.1409380605
median0.158907537
Q30.1851836353
95-th percentile0.2202095628
Maximum0.293139123
Range0.187330192
Interquartile range (IQR)0.04424557475

Descriptive statistics

Standard deviation0.03197074631
Coefficient of variation (CV)0.1958021254
Kurtosis0.1461595536
Mean0.1632808952
Median Absolute Deviation (MAD)0.0218434155
Skewness0.5335825917
Sum2227.477972
Variance0.00102212862
MonotonicityNot monotonic
2022-01-04T14:58:18.033360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1465749622
 
< 0.1%
0.2686131541
 
< 0.1%
0.1142273651
 
< 0.1%
0.153185181
 
< 0.1%
0.1514601921
 
< 0.1%
0.1501923421
 
< 0.1%
0.1487605511
 
< 0.1%
0.1456827971
 
< 0.1%
0.1434353251
 
< 0.1%
0.1397945071
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1058089311
< 0.1%
0.1058102911
< 0.1%
0.1058116511
< 0.1%
0.1058414931
< 0.1%
0.1062064611
< 0.1%
0.1062065871
< 0.1%
0.1062324231
< 0.1%
0.1062466361
< 0.1%
0.1062467481
< 0.1%
0.106424921
< 0.1%
ValueCountFrequency (%)
0.2931391231
< 0.1%
0.2922734421
< 0.1%
0.2912409641
< 0.1%
0.2895739031
< 0.1%
0.2866927061
< 0.1%
0.2842183311
< 0.1%
0.2835381451
< 0.1%
0.2824357681
< 0.1%
0.2818440821
< 0.1%
0.2810648111
< 0.1%

1.5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13640
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1643248101
Minimum0.105755984
Maximum0.297555348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:18.255888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.105755984
5-th percentile0.1149316845
Q10.139484042
median0.1600037605
Q30.1862867563
95-th percentile0.2274192669
Maximum0.297555348
Range0.191799364
Interquartile range (IQR)0.04680271425

Descriptive statistics

Standard deviation0.03423508514
Coefficient of variation (CV)0.2083378957
Kurtosis0.2475717916
Mean0.1643248101
Median Absolute Deviation (MAD)0.023238827
Skewness0.6475210363
Sum2241.719059
Variance0.001172041054
MonotonicityNot monotonic
2022-01-04T14:58:18.478415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1332021642
 
< 0.1%
0.1555838932
 
< 0.1%
0.2826594681
 
< 0.1%
0.1122976311
 
< 0.1%
0.1518287811
 
< 0.1%
0.1500682221
 
< 0.1%
0.1483158311
 
< 0.1%
0.1450736081
 
< 0.1%
0.1426560671
 
< 0.1%
0.1390673491
 
< 0.1%
Other values (13630)13630
99.9%
ValueCountFrequency (%)
0.1057559841
< 0.1%
0.1057566951
< 0.1%
0.1057574831
< 0.1%
0.1057908951
< 0.1%
0.1061324321
< 0.1%
0.1066461611
< 0.1%
0.1066506721
< 0.1%
0.1066510711
< 0.1%
0.1066807361
< 0.1%
0.107026391
< 0.1%
ValueCountFrequency (%)
0.2975553481
< 0.1%
0.2972502321
< 0.1%
0.2962870451
< 0.1%
0.295570771
< 0.1%
0.2945792051
< 0.1%
0.2926649821
< 0.1%
0.290825991
< 0.1%
0.2899298211
< 0.1%
0.2899014761
< 0.1%
0.2894496061
< 0.1%

1.6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1658789495
Minimum0.106092392
Maximum0.305899937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:18.698015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.106092392
5-th percentile0.1154044257
Q10.1386058
median0.1602824075
Q30.186490072
95-th percentile0.2380970523
Maximum0.305899937
Range0.199807545
Interquartile range (IQR)0.047884272

Descriptive statistics

Standard deviation0.03699748216
Coefficient of variation (CV)0.2230390431
Kurtosis0.4476811669
Mean0.1658789495
Median Absolute Deviation (MAD)0.023897659
Skewness0.8010563239
Sum2262.920629
Variance0.001368813686
MonotonicityNot monotonic
2022-01-04T14:58:18.905903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1252314212
 
< 0.1%
0.2961715121
 
< 0.1%
0.1123092681
 
< 0.1%
0.1506354541
 
< 0.1%
0.1485716721
 
< 0.1%
0.1451616441
 
< 0.1%
0.1425645451
 
< 0.1%
0.1390240841
 
< 0.1%
0.1362473591
 
< 0.1%
0.1342223821
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1060923921
< 0.1%
0.1060925621
< 0.1%
0.1060928751
< 0.1%
0.106129121
< 0.1%
0.1064278281
< 0.1%
0.1069467781
< 0.1%
0.1069524521
< 0.1%
0.106952661
< 0.1%
0.1069855651
< 0.1%
0.107317261
< 0.1%
ValueCountFrequency (%)
0.3058999371
< 0.1%
0.3044513571
< 0.1%
0.3042526411
< 0.1%
0.3034867351
< 0.1%
0.3031796481
< 0.1%
0.3031296131
< 0.1%
0.3017975171
< 0.1%
0.3006440121
< 0.1%
0.3006307621
< 0.1%
0.300497991
< 0.1%

1.7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13639
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1677921054
Minimum0.106715272
Maximum0.316892075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:19.134287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.106715272
5-th percentile0.1160252327
Q10.1382173947
median0.160518977
Q30.1877323865
95-th percentile0.2506184439
Maximum0.316892075
Range0.210176803
Interquartile range (IQR)0.04951499175

Descriptive statistics

Standard deviation0.03999004688
Coefficient of variation (CV)0.2383309202
Kurtosis0.6705754601
Mean0.1677921054
Median Absolute Deviation (MAD)0.0247409345
Skewness0.9509108649
Sum2289.019902
Variance0.00159920385
MonotonicityNot monotonic
2022-01-04T14:58:19.330463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1441580252
 
< 0.1%
0.2114166832
 
< 0.1%
0.1229095822
 
< 0.1%
0.3090451761
 
< 0.1%
0.114627091
 
< 0.1%
0.1517020041
 
< 0.1%
0.1493452551
 
< 0.1%
0.1457711321
 
< 0.1%
0.1429942271
 
< 0.1%
0.1394969751
 
< 0.1%
Other values (13629)13629
99.9%
ValueCountFrequency (%)
0.1067152721
< 0.1%
0.1067153481
< 0.1%
0.106715621
< 0.1%
0.1067537311
< 0.1%
0.107015641
< 0.1%
0.1075937281
< 0.1%
0.1075999961
< 0.1%
0.1076006921
< 0.1%
0.1076358081
< 0.1%
0.1079294981
< 0.1%
ValueCountFrequency (%)
0.3168920751
< 0.1%
0.3156892371
< 0.1%
0.3143092011
< 0.1%
0.3132849931
< 0.1%
0.3129843581
< 0.1%
0.3129194781
< 0.1%
0.3127155441
< 0.1%
0.3122857971
< 0.1%
0.3119176161
< 0.1%
0.3117975911
< 0.1%

1.8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13641
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1699550217
Minimum0.107545276
Maximum0.328163762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:19.539330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.107545276
5-th percentile0.1171079615
Q10.1382773047
median0.161215526
Q30.1892401805
95-th percentile0.2642118874
Maximum0.328163762
Range0.220618486
Interquartile range (IQR)0.05096287575

Descriptive statistics

Standard deviation0.04304658648
Coefficient of variation (CV)0.2532822276
Kurtosis0.8727070909
Mean0.1699550217
Median Absolute Deviation (MAD)0.025404409
Skewness1.07925749
Sum2318.526405
Variance0.001853008608
MonotonicityNot monotonic
2022-01-04T14:58:19.736480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1293132932
 
< 0.1%
0.3212595011
 
< 0.1%
0.1161547241
 
< 0.1%
0.1561813531
 
< 0.1%
0.1531192831
 
< 0.1%
0.1504916341
 
< 0.1%
0.1467607951
 
< 0.1%
0.1438093121
 
< 0.1%
0.1403496661
 
< 0.1%
0.1376102791
 
< 0.1%
Other values (13631)13631
99.9%
ValueCountFrequency (%)
0.1075452761
< 0.1%
0.1075456671
< 0.1%
0.10754631
< 0.1%
0.107585451
< 0.1%
0.1078158441
< 0.1%
0.1084898581
< 0.1%
0.1084965811
< 0.1%
0.1084976651
< 0.1%
0.1085344821
< 0.1%
0.1087956651
< 0.1%
ValueCountFrequency (%)
0.3281637621
< 0.1%
0.3280980781
< 0.1%
0.3256177241
< 0.1%
0.3253879331
< 0.1%
0.3253148021
< 0.1%
0.3243261091
< 0.1%
0.3240842451
< 0.1%
0.3239519091
< 0.1%
0.3232347831
< 0.1%
0.3231500261
< 0.1%

1.9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct13642
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.172287154
Minimum0.108522839
Maximum0.340378351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2022-01-04T14:58:19.937539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.108522839
5-th percentile0.1182492779
Q10.1386853258
median0.162009476
Q30.1914474262
95-th percentile0.2768925057
Maximum0.340378351
Range0.231855512
Interquartile range (IQR)0.0527621005

Descriptive statistics

Standard deviation0.04607215676
Coefficient of variation (CV)0.2674149272
Kurtosis1.040367009
Mean0.172287154
Median Absolute Deviation (MAD)0.026289017
Skewness1.183207751
Sum2350.341355
Variance0.002122643629
MonotonicityNot monotonic
2022-01-04T14:58:20.167872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3328314911
 
< 0.1%
0.1865533231
 
< 0.1%
0.1582001141
 
< 0.1%
0.1547764131
 
< 0.1%
0.1519002781
 
< 0.1%
0.1480215981
 
< 0.1%
0.1449035651
 
< 0.1%
0.1414759161
 
< 0.1%
0.1387482591
 
< 0.1%
0.1367408341
 
< 0.1%
Other values (13632)13632
99.9%
ValueCountFrequency (%)
0.1085228391
< 0.1%
0.1085234861
< 0.1%
0.1085244111
< 0.1%
0.1085643351
< 0.1%
0.108767721
< 0.1%
0.1095615431
< 0.1%
0.1095686121
< 0.1%
0.1095700011
< 0.1%
0.109608121
< 0.1%
0.1098415271
< 0.1%
ValueCountFrequency (%)
0.3403783511
< 0.1%
0.3392655051
< 0.1%
0.3375356191
< 0.1%
0.3368625241
< 0.1%
0.3367292851
< 0.1%
0.3361540691
< 0.1%
0.3361494061
< 0.1%
0.3359655151
< 0.1%
0.3354346631
< 0.1%
0.3347329631
< 0.1%

Interactions

2022-01-04T14:58:07.399215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:03.914831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.359140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.719722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.265844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:17.906202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:21.340149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:24.850956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:28.282388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.659298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:34.872295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:38.211105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:41.841833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:45.490728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:49.400587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:53.307520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:56.976498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:00.697990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:04.048603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:07.570017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:04.223248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.519204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.889545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.447380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:18.077981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:21.509974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:25.020785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:28.448311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.820338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:35.182662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:38.390694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:42.020440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:45.686904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:49.585051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:53.489056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:57.159986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:00.861966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:04.213546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:07.732031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:04.383312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.678290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:11.057434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.619157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:18.253658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:21.673944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:25.181821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:28.606423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.978452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:35.336164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:38.560515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:42.193190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:45.870391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:49.778302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:53.676450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:57.357139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:01.018120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:04.373610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:07.909663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:04.568751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.855923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:11.237978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.807524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:18.439101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:22.000904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:25.362381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:28.776245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:32.150228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:35.505990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:38.746933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:42.385466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:46.065590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:50.021323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:53.887269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:57.551363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:01.189912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:04.546365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:08.099007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:04.754189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:08.034530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:11.429281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.996883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:18.631373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-01-04T14:58:03.204362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:06.424189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:10.009042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:06.679841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.055829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:13.570935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:17.028902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:20.634502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:24.166781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:27.605231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:30.992689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:34.210565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:37.556566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:41.125447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:44.764954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:48.627597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:52.407649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:56.220885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:59.955254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:03.387850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:06.743346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:10.195457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:06.861377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.235413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:13.758323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:17.221176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:20.829702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:24.353194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:27.790672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.175203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:34.389174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:37.733244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:41.320646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:44.955273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:48.850123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:52.608706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:56.433651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:00.152405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:03.564507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:06.922925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:10.351618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.026322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.396456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:13.926197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:17.554966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:21.003430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:24.520092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:27.954640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.333317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:34.548265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:37.890980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:41.494376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:45.124727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:49.039468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:52.785361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:56.616165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:00.337858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:03.723596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:07.078113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:10.516562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:07.185410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:10.552613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:14.092115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:17.734552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:21.168377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:24.684059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:28.113728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:31.492402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:34.706377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:38.050069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:41.665174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:45.304310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:49.217100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:52.959088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:57:56.793010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:00.512551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:03.883659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-04T14:58:07.238182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-04T14:58:20.385520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-04T14:58:20.679297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-04T14:58:21.107761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-04T14:58:21.392755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-04T14:58:10.942103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-04T14:58:11.482802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateTenor0.10.20.30.40.50.60.70.80.911.11.21.31.41.51.61.71.81.9
01/5/20172M0.4682140.4192510.3762600.3381580.3043720.2748230.2500660.2315350.2214040.2209330.2284180.2404370.2543160.2686130.2826590.2961720.3090450.3212600.332831
11/5/20173M0.4584710.4127100.3727170.3374890.3065110.2797140.2575400.2410070.2314340.2294230.2338390.2424110.2531000.2646110.2762440.2876470.2986540.3091960.319254
21/5/20176M0.4103050.3741890.3430450.3160700.2928420.2732310.2573460.2454400.2377260.2341160.2340780.2367650.2412950.2469450.2532000.2597260.2663120.2728310.279207
31/5/20179M0.3852670.3545800.3283600.3058780.2867120.2706430.2575900.2475290.2404010.2360300.2340850.2341210.2356580.2382640.2415860.2453600.2493970.2535640.257773
41/5/20171Y0.3589860.3336080.3121260.2938700.2784080.2654570.2548190.2463390.2398550.2351850.2321090.2303840.2297600.2300030.2309080.2323030.2340520.2360490.238213
51/5/20172Y0.3550530.3308720.3103220.2927310.2776570.2647970.2539370.2449140.2375890.2318200.2274570.2243350.2222780.2211120.2206710.2208080.2213940.2223250.223515
61/5/20173Y0.3532240.3294650.3091760.2916880.2765540.2634620.2521960.2425910.2345200.2278660.2225130.2183400.2152140.2129990.2115570.2107580.2104830.2106290.211107
71/5/20174Y0.3566700.3325980.3119240.2939720.2782820.2645370.2525130.2420470.2330170.2253240.2188780.2135890.2093580.2060790.2036410.2019270.2008270.2002370.200063
81/5/20175Y0.3534240.3295380.3089910.2911090.2754380.2616610.2495540.2389560.2297480.2218340.2151320.2095620.2050370.2014620.1987340.1967460.1953920.1945700.194188
91/5/20176Y0.3440970.3207440.3006350.2831130.2677360.2541950.2422730.2318120.2226990.2148440.2081710.2026060.1980700.1944720.1917170.1897000.1883180.1874700.187064

Last rows

DateTenor0.10.20.30.40.50.60.70.80.911.11.21.31.41.51.61.71.81.9
1363210/14/20196Y0.3243610.3059320.2898440.2756540.2630450.2517800.2416830.2326170.2244760.2171750.2106440.2048230.1996600.1951060.1911180.1876510.1846620.1821090.179950
1363310/14/20197Y0.3195080.3014600.2857110.2718220.2594790.2484480.2385530.2296580.2216580.2144680.2080180.2022500.1971130.1925600.1885490.1850390.1819890.1793600.177114
1363410/14/20198Y0.3145050.2967860.2813290.2677000.2555860.2447560.2350350.2262890.2184140.2113240.2049510.1992380.1941340.1895950.1855800.1820490.1789640.1762890.173988
1363510/14/20199Y0.3103660.2929600.2777820.2644020.2525100.2418750.2323260.2237290.2159780.2089910.2026980.1970440.1919780.1874570.1834410.1798930.1767760.1740560.171699
1363610/14/201910Y0.3069250.2898180.2749070.2617660.2500870.2396430.2302610.2218090.2141820.2072970.2010860.1954920.1904670.1859680.1819550.1783930.1752480.1724860.170075
1363710/14/201915Y0.2904670.2740710.2598190.2472860.2361670.2262380.2173290.2093090.2020770.1955500.1896630.1843590.1795930.1753210.1715080.1681180.1651190.1624810.160172
1363810/14/201920Y0.2797260.2636350.2496950.2374730.2266600.2170300.2084110.2006740.1937160.1874560.1818260.1767720.1722470.1682080.1646170.1614400.1586420.1561940.154063
1363910/14/201925Y0.2756930.2597110.2459160.2338590.2232210.2137690.2053300.1977690.1909810.1848840.1794090.1744990.1701060.1661870.1627030.1596180.1569000.1545170.152440
1364010/14/201930Y0.2716370.2560540.2426480.2309650.2206850.2115710.2034500.1961860.1896730.1838260.1785770.1738670.1696470.1658730.1625070.1595140.1568610.1545180.152458
1364110/14/201940Y0.2665930.2518650.2392460.2282850.2186610.2101440.2025570.1957660.1896660.1841710.1792140.1747360.1706890.1670310.1637260.1607420.1580500.1556240.153441